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Review

A Review of Optimization Studies for System Appointment Scheduling

by
Tiantian Niu
1,
Bingyin Lei
1,
Li Guo
2,
Shu Fang
3,
Qihang Li
4,
Bingrui Gao
5,
Li Yang
6 and
Kaiye Gao
7,8,*
1
School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China
2
School of Economics and Management, University of Science & Technology Beijing, Beijing 100083, China
3
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
4
School of Mathematics and Information Science, Zhongyuan University of Technology, Zhengzhou 450007, China
5
School of Education, Johns Hopkins University, Baltimore, MD 21218, USA
6
School of Reliability and Systems Engineering, Beihang University, Beijing 100083, China
7
School of Economics and Management, Beijing Forestry University, Beijing 100091, China
8
Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Axioms 2024, 13(1), 16; https://doi.org/10.3390/axioms13010016
Submission received: 1 November 2023 / Revised: 13 December 2023 / Accepted: 22 December 2023 / Published: 25 December 2023
(This article belongs to the Special Issue Applied Mathematics, Intelligence and Operations Research)

Abstract

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.
Keywords: appointment scheduling; optimization algorithm; healthcare; system structure; queuing theory; literature bibliometric appointment scheduling; optimization algorithm; healthcare; system structure; queuing theory; literature bibliometric

Share and Cite

MDPI and ACS Style

Niu, T.; Lei, B.; Guo, L.; Fang, S.; Li, Q.; Gao, B.; Yang, L.; Gao, K. A Review of Optimization Studies for System Appointment Scheduling. Axioms 2024, 13, 16. https://doi.org/10.3390/axioms13010016

AMA Style

Niu T, Lei B, Guo L, Fang S, Li Q, Gao B, Yang L, Gao K. A Review of Optimization Studies for System Appointment Scheduling. Axioms. 2024; 13(1):16. https://doi.org/10.3390/axioms13010016

Chicago/Turabian Style

Niu, Tiantian, Bingyin Lei, Li Guo, Shu Fang, Qihang Li, Bingrui Gao, Li Yang, and Kaiye Gao. 2024. "A Review of Optimization Studies for System Appointment Scheduling" Axioms 13, no. 1: 16. https://doi.org/10.3390/axioms13010016

APA Style

Niu, T., Lei, B., Guo, L., Fang, S., Li, Q., Gao, B., Yang, L., & Gao, K. (2024). A Review of Optimization Studies for System Appointment Scheduling. Axioms, 13(1), 16. https://doi.org/10.3390/axioms13010016

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